The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology

2025-11-17

AI Summary

How can high-fidelity, contextually grounded qualitative interviews with students be conducted efficiently in digital learning environments without disrupting the learning process? This study introduces Data-Driven Classroom Interviewing (DDCI), a method that dynamically triggers interviews based on real-time student modeling—including behavioral sensing, affective computing, and self-regulation detection—and implements event-focused, researcher-time-optimized interviewing via the open-source Android application Quick Red Fox (QRF). Its key contribution lies in the first integration of fine-grained learning analytics with situated qualitative interviewing, enabling customizable trigger rules and end-to-end interview management. The project releases both the QRF open-source system and a comprehensive methodology handbook, offering a complete practice framework spanning design, implementation, and analysis. DDCI has been empirically validated across multiple learning sciences studies, demonstrating robustness and practical utility in authentic educational settings.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Data Driven Classroom Interviews (DDCIs) are an interviewing technique that is facilitated by recent technological developments in the learning analytics community. DDCIs are short, targeted interviews that allow researchers to contextualize students' interactions with a digital learning environment (e.g., intelligent tutoring systems or educational games) while minimizing the amount of time that the researcher interrupts that learning experience, and focusing researcher time on the events they most want to focus on DDCIs are facilitated by a research tool called the Quick Red Fox (QRF)--an open-source server-client Android app that optimizes researcher time by directing interviewers to users that have just displayed an interesting behavior (previously defined by the research team). QRF integrates with existing student modeling technologies (e.g., behavior-sensing, affect-sensing, detection of self-regulated learning) to alert researchers to key moments in a learner's experience. This manual documents the tech while providing training on the processes involved in developing triggers and interview techniques; it also suggests methods of analyses.

Problem

• Developing an interview app to contextualize student digital learning interactions
• Optimizing researcher time by targeting key learning behavior moments
• Integrating student modeling technologies to trigger focused classroom interviews

Innovation

• Android app directs interviews to interesting behaviors
• Integrates with student modeling technologies for alerts
• Uses triggers to minimize interruption of learning